Evidence map›Paper›PMID 39071522›Full record

ArticleActa medica Philippina2024

Staffing and Workload in Primary Care Facilities of Selected Geographically Isolated and Disadvantaged Communities in the Philippines.

Laurence Lloyd B Parial, Erwin William A Leyva, Kathryn Lizbeth L Siongco, Luz Barbara P Dones, Alexandra Belle S Bernal, Julianne Anika C Lupisan, Daphne C Santos, Micah Marnie C Diamaoden, And Sheila R Bonito

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Article in Acta medica Philippina, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Laurence Lloyd B ParialCollege of Nursing, University of the Philippines Manila, Manila, Philippines.
Erwin William A LeyvaNational Institute of Nursing Research, National Institutes of Health, Maryland, USA.
Kathryn Lizbeth L SiongcoCollege of Nursing, University of the Philippines Manila, Manila, Philippines.
Luz Barbara P DonesCollege of Nursing, University of the Philippines Manila, Manila, Philippines.
Alexandra Belle S BernalCollege of Nursing, University of the Philippines Manila, Manila, Philippines.
Julianne Anika C LupisanCollege of Nursing, University of the Philippines Manila, Manila, Philippines.
Daphne C SantosFaculty of Pharmacy, University of Santo Tomas, Manila, Philippines.
Micah Marnie C DiamaodenCollege of Nursing, Silliman University, Dumaguete City, Philippines.
And Sheila R BonitoCollege of Nursing, University of the Philippines Manila, Manila, Philippines.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Staffing shortages and health inequities are persistent barriers in the Philippines toward achieving universal health care. To ensure an adequate and responsive health workforce, there is a need to evaluate the Human Resources for Health (HRH) status across health facilities, particularly those in underserved communities. Hence, this study aims to determine the staffing requirements and workload pressure among primary care facilities in selected geographically isolated and disadvantaged areas (GIDAs) in the Philippines. Methods: The study utilized the workload indicators of staffing need (WISN) methodology from the World Health Organization to determine the staffing and workload situation among three health worker cadres (physicians, nurses, and midwives) in the study sites. Particularly, six primary care facilities (four rural health units and two community hospitals) located in Surigao del Norte were involved in the study. WISN-related data (health service statistics, available working time, and health professionals' workload components) were collected through records review, focus group discussions, and key informant interviews. The WISN software was used to analyze the staffing levels and workload pressure in the selected facilities. Results: A total of 40 health workers, including physicians ( Conclusion: There is a need to improve the capacities of health facilities and local government units (LGUs) to engage in evidence-based HRH planning through the WISN methodology. Doing so could improve staffing and workload distribution among health care facilities in the country. Moreover, interorganizational collaboration (DOH, LGUs, and health facilities) should be strengthened to improve delineation and prevent duplication/omission of health services, rationalize HRH distribution and augmentation, and streamline the priority health services based on the local contextual factors.

Indexed as

geographically isolated and disadvantaged areashealth workforce planninghuman resources for healthprimary health careworkload indicators of staffing need

Identifiers

PMID39071522
PMCPMC11272892

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.